Data quality monitoring operation
Learn how to use Data quality monitoring.
Let's show you the ropes for Data quality monitoring.
This image illustrates the stages in Real-Time Data Quality.
While there is no unanimous definition of data quality, we consider that it represents the degree to which a data set fulfills its intended function. Data quality measures are based on data quality attributes like precision, exhaustiveness, consistency, validity, originality, and timeliness. Use such measures to reduce the amount of time and effort required to locate and clean data, provide effective data for business decisions, and facilitate automation.
Identify changes to your enterprise's upstream data or source.
Identify unique outliers among hundreds of attributes connected with a particular entity by filtering a specific dataset using data validation functions (custom rules) to track specific business needs.
Identify bad data by evaluating metrics such as the attribute fill rate to see what is missing or out of place, so you can address any that are out of place or need to be enriched to improve quality.
The topic in this section provides step-by-step instructions for analyzing the data in DQ dashboard charts.